Pure adaptive search in Monte Carlo optimization
Mathematical Programming: Series A and B
Global optimization
Pure adaptive search in global optimization
Mathematical Programming: Series A and B
Hesitant adaptive search for global optimisation
Mathematical Programming: Series A and B
On Uniform Covering, Adaptive Random Search and Raspberries
Journal of Global Optimization
On the Investigation of Stochastic Global Optimization Algorithms
Journal of Global Optimization
Matching Stochastic Algorithms to Objective Function Landscapes
Journal of Global Optimization
Random search optimization approach for highly multi-modal nonlinear problems
Advances in Engineering Software
Parameter optimization in 3D reconstruction on a large scale grid
Parallel Computing
Random search optimization approach for highly multi-modal nonlinear problems
Advances in Engineering Software
Exponential stability analysis and impulsive tracking control of uncertain time-delayed systems
Journal of Global Optimization
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Controlled Random Search (CRS) is a simple population based algorithm which despite its attractiveness for practical use, has never been very popular among researchers on Global Optimization due to the difficulties in analysing the algorithm. In this paper, a framework to study the behaviour of algorithms in general is presented and embedded into the context of our view on questions in Global Optimization. By using as a reference a theoretical ideal algorithm called N-points Pure Adaptive Search (NPAS) some new analytical results provide bounds on speed of convergence and the Success Rate of CRS in the limit once it has settled down into simple behaviour. To relate the performance of the algorithm to characteristics of functions to be optimized, constructed simple test functions, called extreme cases, are used.